Knowledge-based Auto Repair Diagnosis System and Application (original) (raw)

Knowledge-based systems as an aid to computer-aided repair

Microprocessors and Microsystems, 1989

Prinetto* present an AI approach to improving test/repair productivity The paper introduces AI techniques in the field of computer-aided testing (CAT) and computer-aided repair (CAR). It is increasingly important to improve the diagnostic capabilities of automatic test equipment (ATE) and AI can provide suitable solutions. The overall goal is to set up test/ repair environments which guarantee not only high diagnostic capability, but also high productivity. The system presented is based on the loop tester--~ repair station -+ tester. In a conventional approach, data from a repair station are not used to improve the performances of the tester. In the solution described here, data are first validated by the tester and then successively processed to gather new knowledge about real cases. Such knowledge is learned by the tester to improve its symptom interpretation capabilities. Two methodological aspects are relevant to this approach: the knowledge the tester receives from the repair station and the knowledge the tester itself needs to learn from experience. It is thus necessary to organize knowledge in knowledge bases and to use it with a proper reasoning mechanism. AI techniques are used to achieve both goals: on the one hand they allow the problem to be formalized in a rigorous way, and on the other hand they facilitate the generation of an open system in which the introduction of new diagnostic rules is easy and where a feedback from the manufacturing test environment is possible. Thus, AI improves diagnostic capabilities in two ways: it helps the ATE to overcome symptom ambiguity and to learn from experience how to modify the fault location procedures.

Implementing an Expert Diagnostic Assistance System for Car Failure and Malfunction

Applications in fault diagnosis are continuously being implemented to serve different sectors. Car failure detection is a sequence of diagnostic processes that necessitates the deployment of expertise. The Expert System (ES) is one of the leading Artificial Intelligence (AI) techniques that have been adopted to handle such task. This paper presents the imperatives for an ES in developing car failure detection model and the requirements of constructing successful Knowledge-Based Systems (KBS) for such model. In addition, it exhibits the adaptation of the ES in the development of Car Failure and Malfunction Diagnosis Assistance System (CFMDAS). However, CFMDAS development faces many challenges such as collecting the required data for building the knowledge base and performing the inferencing. Furthermore, diagnosis of car faults requires high technical skills and experienced mechanics who are typically scarce and expensive to get. Thus, systems such as CFMDAS can be highly useful in assisting mechanics for failure detection and training purposes. Moreover, capturing and retaining valuable knowledge on such domain yield more accurate and less time consuming models.

Developing Knowledge-Based Systems: Car Failure Detection using Expert System

Information and Knowledge Management, 2014

To develop an expert system describes the knowledge-base of the car failure detection. 19 rule-based of car fault diagnosis expert system using the Visual Basic and Microsoft Access as tools for helping inexperienced mechanic as the decision support system. During the test phase of system it never gave wrong detection according to the rules used. It can be concluded that car failure detection expert system is helpful although it might not give a complete guides and help as a human expert namely mechanical engineer do, but at least the expert system can give a temporary assistance to those who are in need of an instance help. Keywords: knowledge-base system, car failure detection, expert system, rule-based

An Approach towards designing of Car Troubleshooting Expert System

International Journal of Computer Applications, 2010

Car problem detection is a complicated process which demands high level of knowledge and skills. Our aim is to develop an expert system on car maintenance and troubleshooting that is capable of assisting car's owner in dealing with their car problems and troubleshooting them whenever the time is limit and the human expert, also known as mechanics is not available at that very point of time. This paper provides affective design issues concerning the problems while driving a car and will give a logical solution which would help in rectifying those problems. The system would contain various set of rules for detecting different type of failures which can be easily handled by the driver and will give their causes. Here the system mainly deals with the starting problems of car and detecting various other large scale problems.

Development of an Intelligent Car Engine Fault Troubleshooting System (CEFTS

Development of an Intelligent Car Engine Fault Troubleshooting System (CEFTS) , 2016

The mass production and wider use of automobiles and the incorporation of complex electronic technologies all indicate that the control of faults should be an integral part of engine design and usage. This paper discusses an expert system application for troubleshooting car engine faults using Auto-mechanic workshops in Calabar metropolis of Cross River State-Nigeria. The method of fact-finding called knowledge acquisition which is an expert system approach to extract facts was adopted in order to achieve good judgment in the use of heuristics among experts. The results are represented as a set of IF-THEN judgments that expert mechanics can rely mostly on in the troubleshooting process. The system depends on an automated matching process between symptoms and procedures. The paper developed a new prototype named Car Engine Fault Troubleshooting System (CEFTS) using C++ programming platform. The purpose of the developed prototype is to assist motorists and auto mechanics in fault troubleshooting of car engines by providing systematic and step-by-step analysis of failure symptoms and offering maintenance or service advice. The result of this development is expected to introduce a systematic and intelligent method in car engine troubleshooting and maintenance environments and also provides a troubleshooting framework for other researchers to work on.

An Expert System for Car Failure Diagnosis

Car failure detection is a complicated process and requires high level of expertise. Any attempt of developing an expert system dealing with car failure detection has to overcome various difficulties. This paper describes a proposed knowledge-based system for car failure detection. The paper explains the need for an expert system and the some issues on developing knowledge-based systems, the car failure detection process and the difficulties involved in developing the system. The system structure and its components and their functions are described. The system has about 150 rules for different types of failures and causes. It can detect over 100 types of failures. The system has been tested and gave promising results. Keywords—Expert system, car failure diagnosis, knowledge- based system, CLIPS.

Approach towards Car Failure Diagnosis-An Expert System

International Journal of Computer Applications, 2010

The paper presents the processes that play a significant role in the development of an expert system. The research has been done to assist in the designing of an expert system for car failure diagnosis and repairs under constraint like time, place and availability of human expertise. Study of technologies for designing expert systems was undertaken to conclude best means, which are simple and easy, to implement and maintain while developing an expert system.

THE DEVELOPMENT OF AN EXPERT CAR FAILURE DIAGNOSIS SYSTEM WITH BAYESIAN APPROACH

In this study we propose a model of an Expert System to diagnose a car failure and malfunction using Bayesian Approach. An expert car failure diagnosis system is a computer system that uses specific knowledge which is owned by an expert to resolve car problems. Our specific system consists of knowledge base and solution to diagnose failure of car from Toyota Avanza, one of the favorite car used in Indonesia today and applying Bayesian approach for knowing the belief of the solution. We build Knowledge representation techniques of symptoms and solution froman experts using production rules. The experimental results presented and we obtained that the system has been able to perform diagnosis on car failure, giving solution and also gives the probability value of that solution.

Design and Implementation of an Expert Management System for Automobile Fault Detection

International Journal of Advances in Scientific Research and Engineering-IJASRE, 2019

A system has been developed to communicate with the On-Board Diagnostics system of a car using the Controller Area Network communication protocol. The system requests the stored trouble codes that might have been detected by the diagnostics system and sends them to a server. The information is accessed through a web interface. The web interface allows the user to find the solutions in the database together with the detected faults. The database also contains information about trouble codes, such as their symptoms, causes and how to fix them. Monitoring the communications while testing the prototype showed that the system works as intended and can communicate with cars from different manufacturers and extract the data. The prototype system was compared with a commercial scan tool and testing showed that they both produced the same results. The second module of the project deals with the design and implementation of an expert system for car faults diagnosis. The project is motivated by the need to guide car owners and learners' motor mechanics in the maintenance and troubleshooting of motor problems without having to resort to presumptions and conjectures. Particularly, it is expected that the proposed design would ensure that car owners have proper assistance in times of crisis and what's more, will save them from the clutches of exploitative roadside mechanics. For this purpose, a rule-based artificial intelligence (AI) technique was utilized to obtain theoretical and practical expert system parameters, and then a conceptual expert system was designed. The expert system functioning is based on the database of car faults, symptoms and their correction, which make up its knowledge base. The new system was developed using PHP and MySQL database. This language was chosen because of its easy syntax and features for developing web-based applications.

Knowledge-Intensive Decision Support System for Manufacturing Equipment Maintenance

Journal of Digital Information Management, 2020

To ensure continuous production in industrial plants, the high valued manufacturing eqipments should be kept in good working conditions. This brings plants to search for means to control and reduce equipment failures. When faults emerge in plants, appropriate actions for fault diagnosis and reparation must be executed promptly and effectively to prevent large costs due to breakdowns. To provide reliable and effective maintenance support, the aid of advanced decision support technology utilizing previous repair experience is of crucial importance for the expert operators as it provides them valuable troubleshooting clues for new faults. Artificial intelligence (AI) technology, particularly, knowledge-based approach is promising for this domain. It captures efficiency of problem solving expertise from the domain experts; guides the expert operators in rapid fault detection and maintenance. This paper focuses on the design and development of a Knowledge-Intensive Decision Support System (KI-DSS) for Manufacturing Equipment Maintenance in industrial plants to support better maintenance decision and improve maintenance efficiency. With integration of casebased Reasoning and ontology, the Ki-DSS not only carries out data matching retrieval, but also performs semantic associated data access which is important for intelligent knowledge retrieval in decision support system. A case is executed to illustrate the use of the proposed KI-DSS to show the feasibility of our ap proach and the benefit of the ontology support.